title: Random Forest for automatic assessment of heart failure severity in a telemonitoring scenario.
abstract: In this study, we describe an automatic classifier of patients with Heart Failure designed for a telemonitoring scenario, improving the results obtained in our previous works. Our previous studies showed that the technique that better processes the heart failure typical telemonitoring-parameters is the Classification Tree. We therefore decided to analyze the data with its direct evolution that is the Random Forest algorithm. The results show an improvement both in accuracy and in limiting critical errors.
authors.forename: G
authors.initials: G
authors.lastname: Guidi
authors.forename: M C
authors.initials: MC
authors.lastname: Pettenati
authors.forename: R
authors.initials: R
authors.lastname: Miniati
authors.forename: E
authors.initials: E
authors.lastname: Iadanza
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006333:Heart Failure / Q000473:pathology*; D006801:Humans; D008991:Monitoring, Physiologic; D010361:Patients / Q000145:classification; D012720:Severity of Illness Index; D017216:Telemedicine